The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes

Abstract

Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.[Supplemental material is available online at www.genome.org. Data sets and documentation are available in the CCDS database at http://www.ncbi.nlm.nih.gov/CCDS.]One key goal of genome projects is to identify and accurately annotate all protein-coding genes. The resulting annotations add functional context to the sequence data and make it easier to traverse to other rich sources of gene and protein information. Accurately annotating known genes, identifying novel genes, and tracking annotations over time are complex processes that are best achieved through a combination of large-scale computational analyses and expert curation. These methods must (1) process repetitive sequences in multiple categories including retrotransposons, segmental duplications, and paralogs; (2) process variation including copy number variation (CNV) (Feuk et al. 2006) and microsatellites; (3) distinguish functional genes and alleles from pseudogenes; (4) define alternate splice products; and (5) avoid erroneous interpretation based on experimental error.

Identification of a G-Protein Subunit-α11 Gain-of-Function Mutation, Val340Met, in a Family With Autosomal Dominant Hypocalcemia Type 2 (ADH2)

“…2 A ), using the SeqCap EZ Human Exome Library v2.0 (Roche NimbleGen, Madison, WI, USA),15 and DNA sequences determined using a 100‐bp paired‐end read protocol on an Illumina HiSeq2000 platform 15. A minimum of 20× vertical read depth was obtained for >88% of the coding exome, as specified by the consensus coding sequence (CCDS) project,16 in both individuals. Reads were aligned to the Human Sequence version 37d5 (hs37d5) reference genome using Stampy17 and variant calling of single nucleotide variants (SNVs) and short insertions and deletions (indels) was undertaken using Platypus (v0.5.1) 18.…”

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

“…The extent of human-specific genic constraint was estimated for each of the 24 co-expression modules by using the genic protein-coding intolerance scores (RVIS) 34 . RVIS was only calculated for protein-coding genes that had at least one protein-coding transcript that was publically approved among the CCDS Release 9 database 74 , and that had ≥70% of their CCDS realestate adequately covered among the population database adopted in their original manuscript (ESP6500) 34 . This resulted in scores for 16,956 assessable CCDS release 9 genes, thus all RVIS comparisons are restricted to these 16,956 "assessable" genes.…”

DNA replication timing and selection shape the landscape of nucleotide variation in cancer genomes

“…We focussed on genes that have an unambiguous annotation of a coding sequence from the consensus coding sequence project (CCDS) 39 . We removed genes that overlapped with other genes or those whose tested coding sequences were < 100 bp in size.…”

“…We focussed on regions classified concordantly by all of the three gene definitions. Coding regions (coding) were obtained from the consensus CDS project 39 .…”